Abstract : In this Ph.D. thesis, we study new methods to analyse digital fundus images of diabetic patients. In particular, we concentrate on the development of the algorithmic components of an automatic screening system for diabetic retinopathy. The techniques developed can be categorized in: quality assessment and improvement, lesion segmentation and diagnosis. For the first category, we present a fast algorithm to numerically estimate the quality of a single image by employing vasculature and colour-based features; additionally, we show how it is possible to increase the image quality and remove reflection artefacts by merging information gathered in multiple fundus images (which are captured by changing the stare point of the patient). For the second category, two families of lesion are targeted: exudate and microaneurysms; two new algorithms which work on single fundus images are proposed and compared with existing techniques in order to prove their efficacy; in the microaneurysms case, a new Radon transform-based operator was developed. In the last diagnosis category, we have developed an algorithm that diagnoses diabetic retinopathy and diabetic macular edema based on the lesions segmented; starting from a single unseen image, our algorithm can generate a diabetic retinopathy and ma cular edema diagnosis in _22 seconds on a 1.6 GHz machine with 4 GB of RAM; additionally, we show the first results of a macular edema detection algorithm based on multiple fundus images, which can potentially identify the swelling of the macula even when no lesions are visible.